Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:

Oracle recommends using the distance metric associated with the embedding model that generated the vectors. Different metrics—such as cosine, Euclidean, dot product, Manhattan, or Hamming—measure similarity in different ways, and an embedding model is normally trained or intended to be evaluated with a particular metric. Oracle’s AI Vector Search documentation states that it is generally best to match the query distance metric to the metric used to train the embedding model. Oracle also notes that a vector index should be created and searched with the appropriate distance function; using a different function can prevent index use and trigger exact search behavior. Table row count, maintenance schedules, and the presence of JSON attributes do not determine semantic vector geometry. Therefore, option C is the correct guidance. Oracle Docs

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